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Bayesian Estimation of Dynamic Discrete Choice Models

Author

Listed:
  • Susumu Imai

    () (Queen's University)

  • Neelam Jain

    () (Northern Illinois University)

  • Andrew Ching

    () (University of Toronto)

Abstract

We propose a new methodology for structural estimation of dynamic discrete choice models. We combine the Dynamic Programming (DP) solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the parameters simultaneously. As a result, the computational burden of estimating a dynamic model becomes comparable to that of a static model. Another feature of our algorithm is that even though per solution-estimation iteration, the number of grid points on the state variable is small, the number of effective grid points increases with the number of estimation iterations. This is how we help ease the "Curse of Dimensionality". We simulate and estimate several versions of a simple model of entry and exit to illustrate our methodology. We also prove that under standard conditions, the parameters converge in probability to the true posterior distribution, regardless of the starting values.

Suggested Citation

  • Susumu Imai & Neelam Jain & Andrew Ching, 2006. "Bayesian Estimation of Dynamic Discrete Choice Models," Working Papers 1118, Queen's University, Department of Economics.
  • Handle: RePEc:qed:wpaper:1118
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    File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1118.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Bayesian Estimation; Dynamic Discrete Choice Model; Dynamic Programming; Markov Chain Monte Carlo; Bayesian Dynamic Programming Estimation;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • L00 - Industrial Organization - - General - - - General

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